Bibliographic record
Abstract
Movement and resumption both Merge a DP in a nonthematic position and interpret it through a free variable. This predicts their symmetric distribution and the existence of resumption on the ‘core’ A-position of [Spec, TP], or A-resumption. It is argued that the prediction is correct, and mechanics are developed to build both movement and resumption by Merge, Agree, and the interpretation of nonthematic positions. A-resumption on [Spec, TP] falls into two types. When T participates in φ-Agree, the DP Merged in [Spec, TP] must be interpretively linked to the variable identified by φ-Agree. Locality tends to limit the goal to a domain where it must be a copy/gap for Case reasons, so the composition of Agree and Merge results in movement. However, when a finite TP boundary is penetrable to φ-Agree, there surfaces an A-resumption pattern constrained by the locality of φ-Agree, including the copy-raising of English The cat i seems like it i ’s got Spiro’s tongue. When T does not φ-Agree with a DP goal, the location of the variable interpreting [Spec, TP] is unconstrained. This is the situation in Breton, which allows A- resumption structures of the type The boati was shot at it i. The patterns of A- resumption restricted and unrestricted by φ-Agree match parallel patterns found in A′-resumption in recent work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".